Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations360
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory157.6 KiB
Average record size in memory448.3 B

Variable types

Numeric10
Categorical3
Text3

Alerts

grid is highly overall correlated with points and 1 other fieldsHigh correlation
points is highly overall correlated with grid and 3 other fieldsHigh correlation
position is highly overall correlated with points and 2 other fieldsHigh correlation
positionOrder is highly overall correlated with grid and 3 other fieldsHigh correlation
positionText is highly overall correlated with points and 2 other fieldsHigh correlation
raceId is highly overall correlated with resultIdHigh correlation
resultId is highly overall correlated with raceIdHigh correlation
resultId is uniformly distributed Uniform
position is uniformly distributed Uniform
positionOrder is uniformly distributed Uniform
resultId has unique values Unique
grid has 11 (3.1%) zeros Zeros
points has 231 (64.2%) zeros Zeros
laps has 6 (1.7%) zeros Zeros

Reproduction

Analysis started2025-02-23 05:33:19.613453
Analysis finished2025-02-23 05:33:37.275917
Duration17.66 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

resultId
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.5
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:37.428853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.95
Q190.75
median180.5
Q3270.25
95-th percentile342.05
Maximum360
Range359
Interquartile range (IQR)179.5

Descriptive statistics

Standard deviation104.06729
Coefficient of variation (CV)0.57655006
Kurtosis-1.2
Mean180.5
Median Absolute Deviation (MAD)90
Skewness0
Sum64980
Variance10830
MonotonicityStrictly increasing
2025-02-23T05:33:37.663671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
248 1
 
0.3%
246 1
 
0.3%
245 1
 
0.3%
244 1
 
0.3%
243 1
 
0.3%
242 1
 
0.3%
241 1
 
0.3%
240 1
 
0.3%
239 1
 
0.3%
Other values (350) 350
97.2%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
360 1
0.3%
359 1
0.3%
358 1
0.3%
357 1
0.3%
356 1
0.3%
355 1
0.3%
354 1
0.3%
353 1
0.3%
352 1
0.3%
351 1
0.3%

raceId
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1106.9444
Minimum1061
Maximum1143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:37.841893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1061
5-th percentile1061
Q11084
median1112.5
Q31126
95-th percentile1143
Maximum1143
Range82
Interquartile range (IQR)42

Descriptive statistics

Standard deviation25.636134
Coefficient of variation (CV)0.023159369
Kurtosis-1.0561956
Mean1106.9444
Median Absolute Deviation (MAD)18
Skewness-0.36370276
Sum398500
Variance657.21139
MonotonicityIncreasing
2025-02-23T05:33:37.988667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1061 20
 
5.6%
1065 20
 
5.6%
1141 20
 
5.6%
1139 20
 
5.6%
1131 20
 
5.6%
1126 20
 
5.6%
1125 20
 
5.6%
1118 20
 
5.6%
1116 20
 
5.6%
1115 20
 
5.6%
Other values (8) 160
44.4%
ValueCountFrequency (%)
1061 20
5.6%
1065 20
5.6%
1071 20
5.6%
1077 20
5.6%
1084 20
5.6%
1095 20
5.6%
1101 20
5.6%
1107 20
5.6%
1110 20
5.6%
1115 20
5.6%
ValueCountFrequency (%)
1143 20
5.6%
1141 20
5.6%
1139 20
5.6%
1131 20
5.6%
1126 20
5.6%
1125 20
5.6%
1118 20
5.6%
1116 20
5.6%
1115 20
5.6%
1110 20
5.6%

driverId
Real number (ℝ)

Distinct31
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean734.65833
Minimum1
Maximum861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:38.134319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.85
Q1822
median840
Q3848
95-th percentile857
Maximum861
Range860
Interquartile range (IQR)26

Descriptive statistics

Standard deviation276.41211
Coefficient of variation (CV)0.3762458
Kurtosis3.1788367
Mean734.65833
Median Absolute Deviation (MAD)12
Skewness-2.2671346
Sum264477
Variance76403.657
MonotonicityNot monotonic
2025-02-23T05:33:38.285051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
830 18
 
5.0%
847 18
 
5.0%
815 18
 
5.0%
1 18
 
5.0%
840 18
 
5.0%
842 18
 
5.0%
832 18
 
5.0%
839 18
 
5.0%
852 18
 
5.0%
822 18
 
5.0%
Other values (21) 180
50.0%
ValueCountFrequency (%)
1 18
5.0%
4 18
5.0%
8 2
 
0.6%
9 1
 
0.3%
20 6
 
1.7%
807 12
3.3%
815 18
5.0%
817 12
3.3%
822 18
5.0%
825 14
3.9%
ValueCountFrequency (%)
861 3
 
0.8%
860 1
 
0.3%
859 4
 
1.1%
858 9
2.5%
857 12
3.3%
856 2
 
0.6%
855 15
4.2%
854 6
 
1.7%
853 3
 
0.8%
852 18
5.0%

constructorId
Real number (ℝ)

Distinct12
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.366667
Minimum1
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:38.413395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median84
Q3210
95-th percentile214
Maximum215
Range214
Interquartile range (IQR)204

Descriptive statistics

Standard deviation89.166421
Coefficient of variation (CV)0.9448932
Kurtosis-1.6320458
Mean94.366667
Median Absolute Deviation (MAD)79.5
Skewness0.27133599
Sum33972
Variance7950.6507
MonotonicityNot monotonic
2025-02-23T05:33:38.550753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 36
10.0%
131 36
10.0%
6 36
10.0%
1 36
10.0%
214 36
10.0%
117 36
10.0%
3 36
10.0%
210 36
10.0%
213 24
6.7%
51 24
6.7%
Other values (2) 24
6.7%
ValueCountFrequency (%)
1 36
10.0%
3 36
10.0%
6 36
10.0%
9 36
10.0%
15 12
 
3.3%
51 24
6.7%
117 36
10.0%
131 36
10.0%
210 36
10.0%
213 24
6.7%
ValueCountFrequency (%)
215 12
 
3.3%
214 36
10.0%
213 24
6.7%
210 36
10.0%
131 36
10.0%
117 36
10.0%
51 24
6.7%
15 12
 
3.3%
9 36
10.0%
6 36
10.0%

number
Real number (ℝ)

Distinct33
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.177778
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:38.704289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median22
Q344
95-th percentile77
Maximum99
Range98
Interquartile range (IQR)33

Descriptive statistics

Standard deviation23.796293
Coefficient of variation (CV)0.84450567
Kurtosis0.10498694
Mean28.177778
Median Absolute Deviation (MAD)12
Skewness1.0347028
Sum10144
Variance566.26357
MonotonicityNot monotonic
2025-02-23T05:33:38.887405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
55 18
 
5.0%
63 18
 
5.0%
44 18
 
5.0%
22 18
 
5.0%
18 18
 
5.0%
10 18
 
5.0%
31 18
 
5.0%
11 18
 
5.0%
14 18
 
5.0%
4 18
 
5.0%
Other values (23) 180
50.0%
ValueCountFrequency (%)
1 15
4.2%
2 9
2.5%
3 12
3.3%
4 18
5.0%
5 6
 
1.7%
6 6
 
1.7%
7 2
 
0.6%
9 3
 
0.8%
10 18
5.0%
11 18
5.0%
ValueCountFrequency (%)
99 3
 
0.8%
88 1
 
0.3%
81 12
3.3%
77 18
5.0%
63 18
5.0%
55 18
5.0%
50 1
 
0.3%
47 6
 
1.7%
44 18
5.0%
43 3
 
0.8%

grid
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9222222
Minimum0
Maximum20
Zeros11
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:39.034009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8423634
Coefficient of variation (CV)0.58881602
Kurtosis-1.174231
Mean9.9222222
Median Absolute Deviation (MAD)5
Skewness0.019206719
Sum3572
Variance34.13321
MonotonicityNot monotonic
2025-02-23T05:33:39.188743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 18
 
5.0%
13 18
 
5.0%
5 18
 
5.0%
16 18
 
5.0%
15 18
 
5.0%
17 18
 
5.0%
12 18
 
5.0%
1 18
 
5.0%
9 18
 
5.0%
8 18
 
5.0%
Other values (11) 180
50.0%
ValueCountFrequency (%)
0 11
3.1%
1 18
5.0%
2 18
5.0%
3 18
5.0%
4 18
5.0%
5 18
5.0%
6 18
5.0%
7 18
5.0%
8 18
5.0%
9 18
5.0%
ValueCountFrequency (%)
20 13
3.6%
19 14
3.9%
18 17
4.7%
17 18
5.0%
16 18
5.0%
15 18
5.0%
14 17
4.7%
13 18
5.0%
12 18
5.0%
11 18
5.0%

position
Categorical

High correlation  Uniform 

Distinct21
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1
 
18
11
 
18
3
 
18
4
 
18
5
 
18
Other values (16)
270 

Length

Max length2
Median length2
Mean length1.55
Min length1

Characters and Unicode

Total characters558
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
1 18
 
5.0%
11 18
 
5.0%
3 18
 
5.0%
4 18
 
5.0%
5 18
 
5.0%
6 18
 
5.0%
9 18
 
5.0%
8 18
 
5.0%
7 18
 
5.0%
10 18
 
5.0%
Other values (11) 180
50.0%

Length

2025-02-23T05:33:39.358359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 18
 
5.0%
7 18
 
5.0%
15 18
 
5.0%
14 18
 
5.0%
13 18
 
5.0%
12 18
 
5.0%
11 18
 
5.0%
10 18
 
5.0%
2 18
 
5.0%
8 18
 
5.0%
Other values (11) 180
50.0%

Most occurring characters

ValueCountFrequency (%)
1 210
37.6%
2 45
 
8.1%
3 36
 
6.5%
4 36
 
6.5%
5 36
 
6.5%
6 35
 
6.3%
8 35
 
6.3%
7 35
 
6.3%
9 33
 
5.9%
0 27
 
4.8%
Other values (2) 30
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 210
37.6%
2 45
 
8.1%
3 36
 
6.5%
4 36
 
6.5%
5 36
 
6.5%
6 35
 
6.3%
8 35
 
6.3%
7 35
 
6.3%
9 33
 
5.9%
0 27
 
4.8%
Other values (2) 30
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 210
37.6%
2 45
 
8.1%
3 36
 
6.5%
4 36
 
6.5%
5 36
 
6.5%
6 35
 
6.3%
8 35
 
6.3%
7 35
 
6.3%
9 33
 
5.9%
0 27
 
4.8%
Other values (2) 30
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 210
37.6%
2 45
 
8.1%
3 36
 
6.5%
4 36
 
6.5%
5 36
 
6.5%
6 35
 
6.3%
8 35
 
6.3%
7 35
 
6.3%
9 33
 
5.9%
0 27
 
4.8%
Other values (2) 30
 
5.4%

positionText
Categorical

High correlation 

Distinct23
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1
 
18
12
 
18
3
 
18
4
 
18
5
 
18
Other values (18)
270 

Length

Max length2
Median length2
Mean length1.5083333
Min length1

Characters and Unicode

Total characters543
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5

Common Values

ValueCountFrequency (%)
1 18
 
5.0%
12 18
 
5.0%
3 18
 
5.0%
4 18
 
5.0%
5 18
 
5.0%
9 18
 
5.0%
7 18
 
5.0%
8 18
 
5.0%
6 18
 
5.0%
10 18
 
5.0%
Other values (13) 180
50.0%

Length

2025-02-23T05:33:39.510505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 18
 
5.0%
6 18
 
5.0%
15 18
 
5.0%
14 18
 
5.0%
13 18
 
5.0%
12 18
 
5.0%
11 18
 
5.0%
10 18
 
5.0%
2 18
 
5.0%
8 18
 
5.0%
Other values (13) 180
50.0%

Most occurring characters

ValueCountFrequency (%)
1 210
38.7%
2 45
 
8.3%
3 36
 
6.6%
4 36
 
6.6%
5 36
 
6.6%
7 35
 
6.4%
8 35
 
6.4%
6 35
 
6.4%
9 33
 
6.1%
0 27
 
5.0%
Other values (3) 15
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 210
38.7%
2 45
 
8.3%
3 36
 
6.6%
4 36
 
6.6%
5 36
 
6.6%
7 35
 
6.4%
8 35
 
6.4%
6 35
 
6.4%
9 33
 
6.1%
0 27
 
5.0%
Other values (3) 15
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 210
38.7%
2 45
 
8.3%
3 36
 
6.6%
4 36
 
6.6%
5 36
 
6.6%
7 35
 
6.4%
8 35
 
6.4%
6 35
 
6.4%
9 33
 
6.1%
0 27
 
5.0%
Other values (3) 15
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 210
38.7%
2 45
 
8.3%
3 36
 
6.6%
4 36
 
6.6%
5 36
 
6.6%
7 35
 
6.4%
8 35
 
6.4%
6 35
 
6.4%
9 33
 
6.1%
0 27
 
5.0%
Other values (3) 15
 
2.8%

positionOrder
Real number (ℝ)

High correlation  Uniform 

Distinct20
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.5
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:39.644202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q15.75
median10.5
Q315.25
95-th percentile19.05
Maximum20
Range19
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation5.7743067
Coefficient of variation (CV)0.54993398
Kurtosis-1.2060806
Mean10.5
Median Absolute Deviation (MAD)5
Skewness0
Sum3780
Variance33.342618
MonotonicityNot monotonic
2025-02-23T05:33:39.814769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 18
 
5.0%
2 18
 
5.0%
19 18
 
5.0%
18 18
 
5.0%
17 18
 
5.0%
16 18
 
5.0%
15 18
 
5.0%
14 18
 
5.0%
13 18
 
5.0%
12 18
 
5.0%
Other values (10) 180
50.0%
ValueCountFrequency (%)
1 18
5.0%
2 18
5.0%
3 18
5.0%
4 18
5.0%
5 18
5.0%
6 18
5.0%
7 18
5.0%
8 18
5.0%
9 18
5.0%
10 18
5.0%
ValueCountFrequency (%)
20 18
5.0%
19 18
5.0%
18 18
5.0%
17 18
5.0%
16 18
5.0%
15 18
5.0%
14 18
5.0%
13 18
5.0%
12 18
5.0%
11 18
5.0%

points
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.55
Minimum0
Maximum8
Zeros231
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:39.954254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4962925
Coefficient of variation (CV)1.6105113
Kurtosis0.5604833
Mean1.55
Median Absolute Deviation (MAD)0
Skewness1.4054176
Sum558
Variance6.2314763
MonotonicityNot monotonic
2025-02-23T05:33:40.081140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 231
64.2%
3 18
 
5.0%
2 18
 
5.0%
1 18
 
5.0%
8 15
 
4.2%
7 15
 
4.2%
6 15
 
4.2%
5 15
 
4.2%
4 15
 
4.2%
ValueCountFrequency (%)
0 231
64.2%
1 18
 
5.0%
2 18
 
5.0%
3 18
 
5.0%
4 15
 
4.2%
5 15
 
4.2%
6 15
 
4.2%
7 15
 
4.2%
8 15
 
4.2%
ValueCountFrequency (%)
8 15
 
4.2%
7 15
 
4.2%
6 15
 
4.2%
5 15
 
4.2%
4 15
 
4.2%
3 18
 
5.0%
2 18
 
5.0%
1 18
 
5.0%
0 231
64.2%

laps
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.583333
Minimum0
Maximum24
Zeros6
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:40.207195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q118
median19
Q324
95-th percentile24
Maximum24
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.752803
Coefficient of variation (CV)0.24269632
Kurtosis5.1596047
Mean19.583333
Median Absolute Deviation (MAD)2
Skewness-1.9406614
Sum7050
Variance22.589136
MonotonicityNot monotonic
2025-02-23T05:33:40.345903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
19 112
31.1%
24 98
27.2%
17 38
 
10.6%
23 38
 
10.6%
21 20
 
5.6%
18 19
 
5.3%
11 19
 
5.3%
0 6
 
1.7%
2 3
 
0.8%
16 2
 
0.6%
Other values (4) 5
 
1.4%
ValueCountFrequency (%)
0 6
 
1.7%
1 1
 
0.3%
2 3
 
0.8%
8 1
 
0.3%
10 2
 
0.6%
11 19
5.3%
12 1
 
0.3%
16 2
 
0.6%
17 38
10.6%
18 19
5.3%
ValueCountFrequency (%)
24 98
27.2%
23 38
 
10.6%
21 20
 
5.6%
19 112
31.1%
18 19
 
5.3%
17 38
 
10.6%
16 2
 
0.6%
12 1
 
0.3%
11 19
 
5.3%
10 2
 
0.6%

time
Text

Distinct341
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size22.6 KiB
2025-02-23T05:33:40.685521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.8555556
Min length2

Characters and Unicode

Total characters2468
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)94.4%

Sample

1st row25:38.426
2nd row+1.430
3rd row+7.502
4th row+11.278
5th row+24.111
ValueCountFrequency (%)
n 20
 
5.6%
47.798 1
 
0.3%
7.502 1
 
0.3%
11.278 1
 
0.3%
24.111 1
 
0.3%
30.959 1
 
0.3%
43.527 1
 
0.3%
44.439 1
 
0.3%
46.652 1
 
0.3%
47.395 1
 
0.3%
Other values (331) 331
91.9%
2025-02-23T05:33:41.183201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 340
13.8%
+ 322
13.0%
3 224
9.1%
1 211
8.5%
4 196
7.9%
2 178
7.2%
0 175
7.1%
5 171
6.9%
7 159
6.4%
6 147
6.0%
Other values (5) 345
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 340
13.8%
+ 322
13.0%
3 224
9.1%
1 211
8.5%
4 196
7.9%
2 178
7.2%
0 175
7.1%
5 171
6.9%
7 159
6.4%
6 147
6.0%
Other values (5) 345
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 340
13.8%
+ 322
13.0%
3 224
9.1%
1 211
8.5%
4 196
7.9%
2 178
7.2%
0 175
7.1%
5 171
6.9%
7 159
6.4%
6 147
6.0%
Other values (5) 345
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 340
13.8%
+ 322
13.0%
3 224
9.1%
1 211
8.5%
4 196
7.9%
2 178
7.2%
0 175
7.1%
5 171
6.9%
7 159
6.4%
6 147
6.0%
Other values (5) 345
14.0%
Distinct341
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2025-02-23T05:33:41.498959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.7222222
Min length2

Characters and Unicode

Total characters2420
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)94.4%

Sample

1st row1538426
2nd row1539856
3rd row1545928
4th row1549704
5th row1562537
ValueCountFrequency (%)
n 20
 
5.6%
1586224 1
 
0.3%
1545928 1
 
0.3%
1549704 1
 
0.3%
1562537 1
 
0.3%
1569385 1
 
0.3%
1581953 1
 
0.3%
1582865 1
 
0.3%
1585078 1
 
0.3%
1585821 1
 
0.3%
Other values (331) 331
91.9%
2025-02-23T05:33:41.996133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 497
20.5%
8 263
10.9%
6 227
9.4%
2 222
9.2%
5 216
8.9%
9 215
8.9%
7 195
 
8.1%
0 191
 
7.9%
3 184
 
7.6%
4 170
 
7.0%
Other values (2) 40
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 497
20.5%
8 263
10.9%
6 227
9.4%
2 222
9.2%
5 216
8.9%
9 215
8.9%
7 195
 
8.1%
0 191
 
7.9%
3 184
 
7.6%
4 170
 
7.0%
Other values (2) 40
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 497
20.5%
8 263
10.9%
6 227
9.4%
2 222
9.2%
5 216
8.9%
9 215
8.9%
7 195
 
8.1%
0 191
 
7.9%
3 184
 
7.6%
4 170
 
7.0%
Other values (2) 40
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 497
20.5%
8 263
10.9%
6 227
9.4%
2 222
9.2%
5 216
8.9%
9 215
8.9%
7 195
 
8.1%
0 191
 
7.9%
3 184
 
7.6%
4 170
 
7.0%
Other values (2) 40
 
1.7%

fastestLap
Categorical

Distinct24
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
4
42 
17
28 
3
25 
8
24 
6
23 
Other values (19)
218 

Length

Max length2
Median length1
Mean length1.4888889
Min length1

Characters and Unicode

Total characters536
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row14
2nd row17
3rd row17
4th row16
5th row16

Common Values

ValueCountFrequency (%)
4 42
 
11.7%
17 28
 
7.8%
3 25
 
6.9%
8 24
 
6.7%
6 23
 
6.4%
5 23
 
6.4%
18 22
 
6.1%
19 21
 
5.8%
16 20
 
5.6%
9 17
 
4.7%
Other values (14) 115
31.9%

Length

2025-02-23T05:33:42.146873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 42
 
11.7%
17 28
 
7.8%
3 25
 
6.9%
8 24
 
6.7%
6 23
 
6.4%
5 23
 
6.4%
18 22
 
6.1%
19 21
 
5.8%
16 20
 
5.6%
9 17
 
4.7%
Other values (14) 115
31.9%

Most occurring characters

ValueCountFrequency (%)
1 158
29.5%
4 65
12.1%
2 49
 
9.1%
8 46
 
8.6%
7 44
 
8.2%
6 43
 
8.0%
9 38
 
7.1%
3 33
 
6.2%
5 32
 
6.0%
0 10
 
1.9%
Other values (2) 18
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 158
29.5%
4 65
12.1%
2 49
 
9.1%
8 46
 
8.6%
7 44
 
8.2%
6 43
 
8.0%
9 38
 
7.1%
3 33
 
6.2%
5 32
 
6.0%
0 10
 
1.9%
Other values (2) 18
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 158
29.5%
4 65
12.1%
2 49
 
9.1%
8 46
 
8.6%
7 44
 
8.2%
6 43
 
8.0%
9 38
 
7.1%
3 33
 
6.2%
5 32
 
6.0%
0 10
 
1.9%
Other values (2) 18
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 158
29.5%
4 65
12.1%
2 49
 
9.1%
8 46
 
8.6%
7 44
 
8.2%
6 43
 
8.0%
9 38
 
7.1%
3 33
 
6.2%
5 32
 
6.0%
0 10
 
1.9%
Other values (2) 18
 
3.4%
Distinct351
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size22.9 KiB
2025-02-23T05:33:42.466641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.85
Min length2

Characters and Unicode

Total characters2826
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique349 ?
Unique (%)96.9%

Sample

1st row1:30.013
2nd row1:29.937
3rd row1:29.958
4th row1:30.163
5th row1:30.566
ValueCountFrequency (%)
n 9
 
2.5%
1:28.717 2
 
0.6%
1:15.425 1
 
0.3%
1:32.139 1
 
0.3%
1:30.566 1
 
0.3%
1:30.640 1
 
0.3%
1:31.773 1
 
0.3%
1:31.687 1
 
0.3%
1:32.208 1
 
0.3%
1:32.183 1
 
0.3%
Other values (341) 341
94.7%
2025-02-23T05:33:42.964877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 592
20.9%
: 351
12.4%
. 351
12.4%
2 244
8.6%
3 213
 
7.5%
4 200
 
7.1%
0 195
 
6.9%
5 186
 
6.6%
9 147
 
5.2%
6 119
 
4.2%
Other values (4) 228
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 592
20.9%
: 351
12.4%
. 351
12.4%
2 244
8.6%
3 213
 
7.5%
4 200
 
7.1%
0 195
 
6.9%
5 186
 
6.6%
9 147
 
5.2%
6 119
 
4.2%
Other values (4) 228
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 592
20.9%
: 351
12.4%
. 351
12.4%
2 244
8.6%
3 213
 
7.5%
4 200
 
7.1%
0 195
 
6.9%
5 186
 
6.6%
9 147
 
5.2%
6 119
 
4.2%
Other values (4) 228
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 592
20.9%
: 351
12.4%
. 351
12.4%
2 244
8.6%
3 213
 
7.5%
4 200
 
7.1%
0 195
 
6.9%
5 186
 
6.6%
9 147
 
5.2%
6 119
 
4.2%
Other values (4) 228
 
8.1%

statusId
Real number (ℝ)

Distinct8
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3361111
Minimum1
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2025-02-23T05:33:43.076277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum130
Range129
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.447963
Coefficient of variation (CV)4.0310298
Kurtosis67.000771
Mean3.3361111
Median Absolute Deviation (MAD)0
Skewness7.7832815
Sum1201
Variance180.84772
MonotonicityNot monotonic
2025-02-23T05:33:43.203937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 340
94.4%
31 10
 
2.8%
3 3
 
0.8%
130 3
 
0.8%
76 1
 
0.3%
10 1
 
0.3%
23 1
 
0.3%
43 1
 
0.3%
ValueCountFrequency (%)
1 340
94.4%
3 3
 
0.8%
10 1
 
0.3%
23 1
 
0.3%
31 10
 
2.8%
43 1
 
0.3%
76 1
 
0.3%
130 3
 
0.8%
ValueCountFrequency (%)
130 3
 
0.8%
76 1
 
0.3%
43 1
 
0.3%
31 10
 
2.8%
23 1
 
0.3%
10 1
 
0.3%
3 3
 
0.8%
1 340
94.4%

Interactions

2025-02-23T05:33:35.346971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:20.355265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:22.325329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:23.575413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:24.914091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:26.297770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:27.684033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:29.123263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:31.143421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:33.321308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:35.500494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:20.606758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:22.461720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:23.701665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:25.052291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:26.459554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:27.833484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:29.264309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:31.286581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:33.603088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:35.658390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:20.846545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:22.570899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:23.818328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:25.180359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:26.590489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:27.971829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:29.433446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:31.456642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:33.852397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:35.786426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:21.093808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:22.684749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:23.938073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:25.313977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:26.708791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:28.108567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:29.571338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:31.593873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:34.101013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:35.915437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:21.280715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:22.799782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:24.058814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:25.454201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:26.834840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:28.242181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:30.272245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:31.762617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:34.338075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:36.098419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:21.557059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:22.919247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:24.201959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:25.579481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:26.958557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:28.404104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:30.424175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:32.021511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:34.582297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:36.238627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:21.752779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:23.050619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:24.363130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:25.716734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:27.097183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:28.558456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:30.575439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:32.306246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:34.819249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:36.387367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:21.898260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:23.191950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:24.528915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:25.886940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:27.255427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:28.699807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:30.716486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:32.573385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:34.956368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:36.569659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:22.045734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:23.318847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:24.661051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:26.027248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:27.396854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:28.848211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:30.866071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:32.851203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:35.090110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:36.704367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:22.182787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:23.448554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:24.784875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:26.152827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:27.553698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:28.981045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:31.006032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:33.058003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T05:33:35.212704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-23T05:33:43.336285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
constructorIddriverIdfastestLapgridlapsnumberpointspositionpositionOrderpositionTextraceIdresultIdstatusId
constructorId1.000-0.2140.0000.260-0.0070.116-0.2670.2420.2440.2350.0130.0270.025
driverId-0.2141.0000.0000.1910.0070.084-0.1400.0510.1820.1330.0760.086-0.032
fastestLap0.0000.0001.0000.0000.4810.0650.0000.0980.1540.0810.3600.3540.118
grid0.2600.1910.0001.000-0.0090.062-0.6640.3930.6490.391-0.072-0.036-0.010
laps-0.0070.0070.481-0.0091.0000.0040.0820.245-0.0980.2630.1190.114-0.355
number0.1160.0840.0650.0620.0041.000-0.0130.1760.0030.1650.0610.061-0.099
points-0.267-0.1400.000-0.6640.082-0.0131.0000.864-0.8250.8600.1400.094-0.175
position0.2420.0510.0980.3930.2450.1760.8641.0000.9760.9970.0000.0000.297
positionOrder0.2440.1820.1540.649-0.0980.003-0.8250.9761.0000.9730.0000.0550.370
positionText0.2350.1330.0810.3910.2630.1650.8600.9970.9731.0000.0000.0000.317
raceId0.0130.0760.360-0.0720.1190.0610.1400.0000.0000.0001.0000.998-0.037
resultId0.0270.0860.354-0.0360.1140.0610.0940.0000.0550.0000.9981.000-0.016
statusId0.025-0.0320.118-0.010-0.355-0.099-0.1750.2970.3700.317-0.037-0.0161.000

Missing values

2025-02-23T05:33:36.942022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-23T05:33:37.149992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

resultIdraceIddriverIdconstructorIdnumbergridpositionpositionTextpositionOrderpointslapstimemillisecondsfastestLapfastestLapTimestatusId
011061830933211131725:38.4261538426141:30.0131
1210611131441222217+1.4301539856171:29.9371
231061822131773333117+7.5021545928171:29.9581
3410618446164444017+11.2781549704161:30.1631
451061846146555017+24.1111562537161:30.5661
561061817137666017+30.9591569385171:30.6401
67106142141411777017+43.5271581953171:31.7731
78106120117510888017+44.4391582865171:31.6871
8910618473638999017+46.6521585078171:32.2081
91010618392143113101010017+47.3951585821161:32.1831
resultIdraceIddriverIdconstructorIdnumbergridpositionpositionTextpositionOrderpointslapstimemillisecondsfastestLapfastestLapTimestatusId
350351114341171411111111019+19.2041642214191:24.2811
3513521143822157713121212019+23.3511646361121:25.4471
35235311438401171814131313019+24.4211647431181:25.3691
35335411438392143117141414019+30.3791653389191:25.5981
354355114384832312151515019+33.0621656072181:25.4431
35535611438592153010161616019+34.3561657366181:25.7621
35635711438522152216171717019+35.1021658112171:25.8381
35735811438613430181818019+35.6391658649181:25.5991
3583591143855152418191919019+1:11.4361694446191:25.0511
35936011438159110202020019+1:14.3711697381181:24.8921